Machine learning techniques are utilized for early detection of diseases, which can significantly enhance probabilities of positive treatment and existence. The traditional machine learning algorithms may be unable to predict outcomes with sufficient accuracy. In this work, an Effective ECOLASSO with Black Widow Optimization for Feature Selection and Stagewise Adaptive Learning Rate (ELBWOSALR) classifier is proposed for feature selection and prediction. The proposed work comprises two phases, in the first phase, Ecological similarity Least Absolute Shrinkage and Selection Operator (ECOLASSO) model is utilized to predict the best features from the dataset by removing the feature with smallest absolute regression coefficient from the feature set. A Black Widow Optimizer (BWO) is used to choose the subset of optimal features and to reduce local optima. In the second phase, Stagewise Adaptive Learning Rate (SALR) involves combining several weak learner classifiers into a strong ensemble classifier by adaptive learning rate. The key contribution of this work is the integration of ECOLASSO model with BWO for robust feature selection, combined with a SALR classifier. This hybridization addresses two critical challenges simultaneously: (i) ECOLASSO ensures sparsity and ecological similarity-driven selection of relevant features, while (ii) BWO prevents premature convergence and enhances global search efficiency. By coupling these with SALR, our model achieves superior accuracy and generalization compared to conventional classifiers. Lung cancer, breast cancer and heart disease datasets are used for experimentation. The ELBWOSALR classifier is compared with various classifier models such as Support Vector Classifier, Decision Tree Classifier, Random Forest Classifier, Logistic Regression, Extreme Gradient Boost Classifier, Gradient Boosting Classifier, K-Nearest Neighbors Classifier and CatBoost Classifier and the results are observed. The proposed ELBWOSALR classifier achieves accuracies of 98%, 97% and 91% with AUC values of 92%, 99% and 94% for lung cancer, breast cancer and heart disease datasets respectively.